The proposition to interpret IML methods more as statistical diagnosis tools was already mentioned by [1]}, but they make no more far-reaching statement about how to accomplish this or what the implications would be. While proposed frameworks work great for method selection and cover the practical procedure of experiment planning, they do not offer a general formalism in which they are embedded. Formalisms in science describe the symbolic manner in which information is presented [2]}. Statistical formalism differs in the sense that it does not infer its formal system based on axioms, but based on a standardized procedure. This procedure can be summarized by means of sampling, modelling, and testing. See [3]} for a detailed definition. Sampling is defined as the selection of a subset of observations from the population which yields optimally the same characteristics as the whole population. Modelling describes the practice of either descriptive analysis, summarizing and making statements about the sample data, or inference, drawing conclusion about causality which hold in general also for the population. When interpreted in a broader spectrum, testing includes the definition of hypotheses, error types, confidence, and significance. Testing is important to counter the miss-use and miss-interpretation of statistics, especially when using ill-defined methods, inducing bias.
